. Object recognition is the classi#cation of objects into one of many a priori known object classes. In addition, it mayinvolve the estimation of the pose of the object and#or the track of the object in a sequence of images. Bayesian statistical pattern recognition, neural networks and rule based systems have been used to address the object recognition problem. In the case of statistical pattern recognition it is assumed that the a priori probability density functions are known or that they can be estimated from the given samples. For neural networks the samples may be used to train a network and the coe#cients for the network function may be estimated. Whereas, in the case of the rule based system, rules may be given by an expert or they may be estimated from the samples. However, Bayesian framework provides a methodology for the estimation of error bounds on the performance of the recognition system. The paper discusses the Bayesian paradigm and contrasts its ability to...